A second set of issues centers on more nuts-and-bolts aspects of how learning occurs. One important challenge, in this regard, is long-term temporal credit assignment, that is, updating behaviour based on rewards that may not accrue until a substantial time after the actions that were responsible for generating them. This remains a challenge for deep RL systems. Novel algorithms have recently been proposed [1]}, but the problem is far from solved, and a dialogue with neuroscience in this area may be beneficial to both fields.
